Video denoising using higher order optimal space-time adaptation

Abstract

The optimal spatial adaptation (OSA) method [1] proposed by Boulanger and Kervrann has proven to be quite effective for spatially adaptive image denoising. This method, in addition to extending the Non-Local Means(NLM) method of [2], employs an iteratively growing window scheme, and a local estimate of the mean square error to very effectively remove noise from images. By adopting an iteratively growing space-time window, the method was recently extended to 3-D for video denoising in [3]. In the present paper, we demonstrate a simple, but effective improvement on the OSA method in both 2-and 3-D. We demonstrate that the OSA implicitly relies on a locally constant model of the underlying signal. Thereby, removing this constraint and introducing the possibility of higher order local regression models, we arrive at a relatively simple modification that results in an improvement in performance. While this improvement is observed in both 2-D and 3-D, we concentrate on demonstrating it in 3-D for the application of video denoising.